It is known that EGM signals can be collected by use of electrodes placed on endocardial or epicardial leads of a device implanted in a patient. These signals, directly related to the electrical activity of cardiac cells of the patient, provide useful information for the purpose of assessing the patient's condition. Hence, after amplifying, conditioning, digitizing and filtering, EGM signals are mainly utilized to control the implanted devices and diagnose rhythm disorders requiring, for example, automatic triggering of an antitachycardia, antibradycardia, or interventricular resynchronization therapy.
However, when it comes to analyzing subjectively the heart rhythm, e.g., to perform a diagnosis or readjust the control/operating parameters of an implanted device, the practitioners prefer, in practice, to interpret the information given by a surface electrocardiogram (ECG). An ECG allows one to visualize in a direct manner, a certain number of determining factors (e.g., QRS width) and thereby assess the evolution of a heart failure.
ECG signals are usually recorded over a long period of time through ambulatory practice by Holter recorders. The recorded ECG signals are then further processed and analyzed in order to evaluate the clinical condition of the patient and eventually diagnose whether a cardiac rhythm disorder is present.
The ECG and EGM signals actually have the same signal source (i.e., the electrical activity of myocardium), however, they visually appear in much different manners: the EGM collected by the implantable device provides local information on the electrical activity of a group of heart cells, whereas the ECG appears in the form of more global information, in particular influenced by the propagation of the electrical signals between the myocardium and body surface, with certain morphologic and pathologic specificities. Thus, the display of EGM signals is not very useful to a practitioner who interprets ECG signals.
When a patient implanted with a medical device comes to his practitioner for a routine visit, two distinct devices are used: an ECG recorder and an external implant programmer. In order to collect the ECG signal, the practitioner places electrodes in particular locations relative to the patient's torso. The ECG signals are collected between predefined pairs of electrodes to define typically twelve “derivations” of the collected ECG signals. The external programmer is used to control certain operating parameters of the implantable device (e.g., the battery life), download data from the implantable device memory, modify the parameters thereof, or upload an updated version of the device operating software, etc.
The visit with the practitioner therefore usually requires these two different devices, as well as specific manipulations for placing the surface electrodes and collecting the ECG signals. Moreover, the use of these two devices requires the patient to come to a specifically equipped center, usually having the consequence that routine visits are spaced farther apart, resulting in a less rigorous follow-up of the patient.
In order to overcome such drawbacks, algorithms have been developed for reconstructing a surface ECG signal based upon collected EGM signals from an implantable device. Some of these algorithms use a neural network. Their functioning is described hereafter in reference to FIGS. 1 and 2, respectively: a first step of learning or developing a transfer function for a neural network and a second step of calculating or reconstructing an ECG signal from a collected EGM signal using a transfer function. EGM signal 1 and ECG signal 3 are collected from the patient and transmitted to neural network 5. Neural network 5 learns and develops a transfer function that delivers ECG signal 3 as an output when the EGM signal 1 is provided as input. When the learning step is done, then for another EGM signal 7 collected from a patient, ECG signal 9 is reconstructed (FIG. 2) as an output signal of this neural network 5 after its transfer function has processed input EGM signal 7.
The EP patent EP 0 784 996 A1 and its US counterpart U.S. Pat. No. 5,740,811, to Hedberg, et al., propose to synthesize an ECG signal by combining a plurality of EGM signals by means of a neural network and/or fuzzy logic and/or summer circuit, after a learning process performed by an algorithm of a “feedforward” type. Such a prior art network 16 is schematically shown in FIG. 3. Network 16 consists of: a set of inputs 20; a so-called “hidden” layer of neurons or “internal” layer of neurons 21, the inputs and outputs of this layer being not linked with EGM input data or ECG output data, and a layer of output neurons 22. Transfer function FT is calculated in the hidden layer 21 and in the output layer 22. Network 16 receives at its various EGM signals 10, 12 and 14 from different derivations 11, 13 or 15. Network 16 processes these EGM signals 10, 12, and 14 using transfer function FT, with sub-functions f, g, . . . k, and generates a reconstructed ECG signal 17.
Network 16 learns, as described with reference to FIGS. 1 and 2, transfer function FT by operating a base of learning associated with a template of a specific signal class. Indeed, the learning of neural network 16 is performed between a single ECG beat and a single EGM beat.
Such a solution is not, however, robust. For example, changes in the QT interval during an exercise (e.g., shortening of QT) are unlikely to be taken into account. In addition, if a new morphology beat is collected (e.g., a ventricular extrasystole, junctional beats, or a ventricular originated arrhythmia), network 16 corresponds to an unknown type or template. But neural network 16 cannot translate its learning based on a known type or template to the synthesis of an unknown type or template.
Moreover, network 16 reconstructs ECG signals beat by beat and requires an intermediate step to concatenate the reconstructed beats, which implicates two major problems related to the length of the window of analysis and to the processing of premature events. The reconstruction also requires a preliminary step to detect the QRS wave form, which may lead to detection of defects and, therefore, resulting in false ECG reconstruction.
Finally, neural network 16 requires at least two EGM derivations, i.e., a minimum presence of two collection electrodes as well as a reference electrode (e.g., the device case or CAN), or three collection electrodes.
The use of time-delay networks in the medical field is known and described for example by C. Vasquez et al. Atrial Activity Enhancement by Wiener Filtering Using an artificial Neural Network, IEEE Transactions on Biomedical Engineering, Vol. 48, No. 8, August 2001, pp. 940-944, as well as in US 2008/013747 A1 and U.S. Pat. No. 6,572,560 B1. Nevertheless, it is never suggested, and the inventors believe that they are the first to have recognized the possibility to use a time-delay network in generating a reconstructed ECG signal from collected EGM signals directly delivered by an implanted device.